AI Drone Infrastructure Inspection

AI Overview Summary

Ombrulla implemented an AI Drone Infrastructure Inspection workflow for oil and gas asset integrity teams. Drone flights capture high-resolution video of critical infrastructure such as storage tanks and pipelines. Ombrulla’s AI models analyze the footage to detect and classify anomalies including cracks, corrosion, paint/coating loss, insulation damage, and potential leaks. The result is a faster, safer, and more consistent inspection process with traceable visual evidence, enabling earlier intervention and better maintenance prioritization.

• •Process: Infrastructure inspection for oil and gas assets (tanks, pipelines, and associated facilities)
• •Data source: Drone video (visible spectrum; thermal optional based on use case)
• •Anomalies detected: Cracks, corrosion, coating/paint removal, insulation damage, leaks and seepage indicators
• •Typical deployment: Pre-planned drone missions + AI analysis + exception review + maintenance work order integration
• •Primary outcomes: Reduced inspection risk and downtime, faster anomaly identification, audit-ready evidence, improved integrity governance

Business Context

Routine and event-driven visual inspection of critical infrastructure - storage tanks, pipelines, and other facilities - using drone data as the primary capture method.

• •Safety and HSE: Reduce work at height, rope access, scaffolding, and exposure time in hazardous zones.
• •Risk reduction: Detect early-stage degradation before it escalates into leaks, unplanned shutdowns, or reportable incidents.
• •Cost and uptime: Shift from reactive repairs to targeted maintenance planning and fewer emergency interventions.
• •Governance: Provide traceable visual evidence to support integrity programs, audits, and contractor accountability.

The Challenge

Traditional infrastructure inspections in oil and gas often depend on manual walkdowns, rope access teams, scaffolding, and periodic shutdown windows. Even when drones are used, the bottleneck frequently shifts to manual video review.

Slow Review Cycles

Large volumes of video/images take hours or days to analyze and report.

Coverage Constraints

High or confined areas may be sampled rather than fully reviewed due to access limits.

Inconsistent Interpretation

Defect identification varies by reviewer experience and fatigue.

Evidence Gaps

Findings may lack precise location mapping, making reinspection and follow-up slower.

Deferred risk: Minor corrosion, coating failures, or insulation damage can progress between inspection rounds.

The Solution

Ombrulla introduced an AI-driven inspection layer on top of drone capture. Drone missions collect consistent, repeatable video of asset surfaces and components. Ombrulla’s anomaly detection models automatically scan the footage, flagging potential issues and producing a structured inspection output.

The system provides an asset map / digital twin view linking findings to exact locations (tank courses, nozzle zones, pipeline chainage).

How It Works

1. Mission Planning

Define inspection scope by asset type and risk priority. Plan flight paths for repeatable coverage (angles, standoff distance, overlap).

2. Drone Capture

Capture high-resolution video; use thermal payloads where leak/heat anomalies are relevant.

3. AI Anomaly Detection

Models analyze video frames to detect and segment defects like cracks and corrosion. Findings are categorised and scored.

4. Exception Workflow

Integrity engineer reviews flagged anomalies (human-in-the-loop) and confirms true positives.

5. Maintenance Action

Confirmed findings are generated as inspection records and routed into SAP/CMMS as work orders.

Measurable Impact

Core value delivered by the drone-video inspection approach.

• •Reduced exposure to high-risk access methods (work at height, confined zones).
• •Faster inspection turnaround by automating first-pass review of drone footage.
• •Higher consistency via standardised anomaly categories and review criteria.
• •Better maintenance prioritisation by ranking anomalies by severity and recurrence.
• •Audit-ready evidence that accelerates compliance reporting and contractor oversight.

Anomaly Coverage

Ombrulla’s models are configured to detect common visual integrity issues:

Cracks

Linear discontinuities on shells, weld regions, supports, and structural members.

Corrosion

Surface rust, pitting indicators, and corrosion under insulation (CUI) visual cues.

Paint/Coating Loss

Coating breakdown, peeling, bare metal exposure, blistering patterns.

Insulation & Leaks

Damaged cladding/insulation, wet sections, staining, drips, or thermal seepage signatures.

Infrastructure in Scope

Assets typically inspected using drones and AI video analytics:

• •Storage tanks (shell, roof, nozzles, stairways, bund walls).
• •Pipelines and pipe racks (supports, expansion loops, crossings, ROW monitoring).
• •Process units and other infrastructure (flare stacks, chimneys, jetties, substations).

Implementation Approach

A typical production-grade rollout phases:

• •Asset register mapping: Define assets, zones, and priorities.
• •Data capture standardisation: Flight plans and image quality guidelines.
• •Model configuration: Taxonomy and severity rules.
• •Pilot run: Parallel manual + AI review to calibrate thresholds.
• •Go-live: SOP updates, training, and integration.

KPIs to Track Post Go-Live

• •Inspection turnaround time (capture-to-report).
• •High-risk access hours avoided.
• •Anomaly detection precision and confirmation rate.
• •Mean time to repair (MTTR) for critical anomalies.
• •Repeat anomaly rate by asset.